Single-step change point estimation in nonlinear profiles using maximum likelihood estimation

Author(s):  
Mahdi Bastan ◽  
Mahdi Hamid ◽  
Ali Ghazizadeh ◽  
Ahmad Taher Azar ◽  
Hashem Mahlooji
2014 ◽  
Vol 53 (3) ◽  
pp. 694-714 ◽  
Author(s):  
V. K. Jandhyala ◽  
P. Liu ◽  
S. B. Fotopoulos ◽  
I. B. MacNeill

AbstractA comprehensive change-point analysis of annual radiosonde temperature measurements collected at the surface, troposphere, tropopause, and lower-stratosphere levels at both the South and North Polar zones has been done. The data from each zone are modeled as a multivariate Gaussian series with a possible change point in both the mean vector as well as the covariance matrix. Prior to carrying out an analysis of the data, a methodology for computing the large sample distribution of the maximum likelihood estimator of the change point is first developed. The Bayesian approach for change-point estimation under conjugate priors is also developed. A simulation study is carried out to compare the maximum likelihood estimator and various Bayesian estimates. Then, a comprehensive change-point analysis under a multivariate framework is carried out on the temperature data for the period 1958–2008. Change detection is based on the likelihood ratio procedure, and change-point estimation is based on the maximum likelihood principle and other Bayesian procedures. The analysis showed strong evidence of change in the correlation between tropopause and lower-stratosphere layers at the South Polar zone subsequent to 1981. The analysis also showed evidence of a cooling effect at the tropopause and lower-stratosphere layers, as well as a warming effect at the surface and troposphere layers at both the South and North Polar zones.


2001 ◽  
Vol 38 (A) ◽  
pp. 122-130 ◽  
Author(s):  
Ali S. Dabye ◽  
Yury A. Kutoyants

Consider an inhomogeneous Poisson process X on [0, T] whose unknown intensity function ‘switches' from a lower function g∗ to an upper function h∗ at some unknown point θ∗. What is known are continuous bounding functions g and h such that g∗(t) ≤ g(t) ≤ h(t) ≤ h∗(t) for 0 ≤ t ≤ T. It is shown that on the basis of n observations of the process X the maximum likelihood estimate of θ∗ is consistent for n →∞, and also that converges in law and in pth moment to limits described in terms of the unknown functions g∗ and h∗.


2016 ◽  
Vol 23 (6) ◽  
pp. 2995-3008
Author(s):  
Reza Baradaran Kazemzadeh ◽  
Amirhossein Amiri ◽  
Hamidreza Mirbeik

1998 ◽  
Vol 14 (2) ◽  
pp. 222-259 ◽  
Author(s):  
Byeongseon Seo

This paper considers tests for structural change of the cointegrating vector and the adjustment vector in the error correction model with an unknown change point. This paper derives new tests for structural change, which are applicable to maximum likelihood estimation. Our tests for structural change of the cointegrating vector have the same nonstandard asymptotic distributions that have been found by Hansen (1992a, Journal of Business and Economic Statistics 10, 321–335). In contrast, the tests on the adjustment vector have the same asymptotic distributions that have been found by Andrews and Ploberger (1994, Econometrica 62, 1383–1414) for models with stationary variables. Asymptotic critical values are provided.


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